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Vertical-cavity surface-emitting laser sources for gigahertz-bandwidth, multiwavelength frequency-domain photon migration [From 2017. Mentioned in one of the "HI LLC" patents.] by starspawn0 in StarspawnsLocker

[–]starspawn0[S] 0 points1 point  (0 children)

Another detail from that patent filing that references the above paper:

https://patents.google.com/patent/US20190336005A1/en

Furthermore, because the frequency response technique used by the optical measurement system 10 does not require holography, in addition to not requiring complex and expensive equipment, the optical non-invasive measurement system 10 does not require the detection of speckles (i.e., the use of highly coherent light and the ability to spatially resolve speckles at the detection plane). As such, it is possible for the current system to utilize very simple optical sources that are partially coherent (e.g., LEDs or VCSEL diodes), as well as large and simple photodiodes to detect this partially coherent light across a large area, thus collecting many more photons per detector than in the case of spatially resolved speckle.

So,

  • Low-cost components.

  • Don't have to bother with speckle decorrelation time.

  • Possibly the ability to measure "fast optical signals", like direct neural activity, and at high resolution?

It involves pulsing light at high frequency (up to 100 GHz), and then measuring how it propagates through tissue, as pulse frequency is varied. And what does one do with that information? Possibly this:

It is also possible to input the intensity and phase data from each source-detector pair into a computer simulation embodying a solver for the frequency-dependent diffusion equation, which solver contains a set of parameters reflective of optical properties at different depths or tissue locations, and then attempt to invert this equation to recover a spatial map of absorption and/or path-length changes across the brain, for example by iteratively adjusting parameters to maximize the likelihood of the intensity and phase data given the simulated solution and a model of the system noise or adjusting such parameters in the manner of gradient descent optimization or other optimization procedures. Compared to prior art, performing this inversion with a large set of frequencies that extend into the multi-GHz regime can improve the spatial resolution of the reconstructed map as well as its sensitivity to changes due to fast optical signals.

Sort of reminds me of the inverse problem faced in EIT (electrical impedance tomography), which deep learning has been applied to:

https://arxiv.org/abs/1906.03944